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https://github.com/sonwanesuresh95/rfm

Python Package for RFM Analysis and Customer Segmentation
https://github.com/sonwanesuresh95/rfm

customer-segmentation python rfm-analysis

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Python Package for RFM Analysis and Customer Segmentation

Host: GitHub
URL: https://github.com/sonwanesuresh95/rfm
Owner: sonwanesuresh95
License: mit
Created: 2022-02-13T12:14:38.000Z (almost 3 years ago)
Default Branch: main
Last Pushed: 2022-03-02T16:40:59.000Z (almost 3 years ago)
Last Synced: 2024-05-14T00:15:12.527Z (7 months ago)
Topics: customer-segmentation, python, rfm-analysis
Language: Python
Homepage:
Size: 3.44 MB
Stars: 10
Watchers: 2
Forks: 1
Open Issues: 3
Metadata Files:
- Readme: README.md
- License: LICENSE

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awesome-marketing-machine-learning - rfm

README

        















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# rfm

rfm: Python Package for RFM Analysis and Customer Segmentation

## Info

**rfm** is a Python package that provides **recency, frequency, monetary analysis** results

for a certain transactional dataset within a snap. Its flexible structure and multiple automated

functionalities provide easy and intuitive approach to RFM Analysis in an automated fashion.

It aims to be a ready-made python package with high-level and quick prototyping.

On practical hand, **real world data** is easily suited and adapted by the package.

Additionally, it can make colorful, intuitive graphs using a matplotlib backend without 

breaking a sweat.

## Installation

### Dependencies



  Python (>=3.7)

  Pandas (>=1.2.4)

  NumPy (>=1.20.1)

  matplotlib (>=3.3.4)



To install the current release (Ubuntu and Windows):

```

$ pip install rfm

```

## Usage

```

# predefine a transaction dataset as df

>>> from rfm import RFM

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')

>>> r.plot_segment_distribution()

```



  




# License

[MIT](https://github.com/sonwanesuresh95/rfm/blob/main/LICENSE)

# Documentation

<-- Temporarily Hosted Here -->

## Initialization

Read required dataframe

```

>>> df = pd.read_csv('~./data.csv')

```

Import RFM package and start rfm analysis automatically:

```

>>> from rfm import RFM

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount') 

>>> r.rfm_table

```

If you want to do rfm analysis manually:

```

>>> r = RFM(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount', automated=False)

```

## Attributes

### RFM.rfm_table

returns resultant rfm table df generated with recency, frequency & monetary values and scores along with segments

```

>>> r.rfm_table

```



  




### RFM.segment_table

returns segment table df with 10 unique categories i.e. Champions, Loyal Accounts etc. 

```

>>> r.segment_table

```



  




## Methods

### RFM.plot_rfm_histograms()

Plots recency, frequency and monetary histograms in a single row

```

>>> r.plot_rfm_histograms()

```



  




### RFM.plot_rfm_order_distribution()

Plots orders by customer number

```

>>> r.plot_rfm_order_distribution()

```



  




### RFM.plot_versace_plot(column1, column2)

Plots scatterplot of two input columns

```

>>> r.plot_versace_plot(column1='recency',column2='monetary_value')

```



  




```

>>> r.plot_versace_plot(column1='recency',column2='frequency')

```



  




```

>>> r.plot_versace_plot(column1='frequency',column2='monetary_value')

```



  




### RFM.plot_distribution_by_segment(column, take)

Plots Distribution of input column by segment

```

>>> r.plot_distribution_by_segment(column='recency',take='median')

```



  




```

>>> r.plot_distribution_by_segment(column='frequency',take='median')

```



  




```

>>> r.plot_distribution_by_segment(column='monetary_value',take='median')

```



  




### RFM.plot_column_distribution(column)

Plots column distribution of input column

```

>>> r.plot_column_distribution(column='recency')

```



  




```

>>> r.plot_column_distribution(column='frequency')

```



  




```

>>> r.plot_column_distribution(column='monetary_value')

```



  




### RFM.plot_segment_distribution()

```

>>> r.plot_segment_distribution()

```

Plots Segment Distribution, i.e. Segments vs no. of customers



  




### RFM.find_customers(segment)

returns rfm results df with input category

```

>>> r.find_customers('Champions')

```